Validation built on S-1 filings, not Reddit threads.
DimeADozen.AI: Sourced data + named comp-set + retention-curve math from public filings — not live-signal scrapers.
What you're actually getting
Every DimeADozen.AI validation report includes:
Market data
Sourced from filings, regulatory data, and named industry reports. Not paraphrased; cited.
Named comp-set
Three publicly-disclosed companies in your category. We name them. We pull their cohort-retention curves at month 3 / 6 / 12 / 24.
Retention-curve math
Your projected retention plotted against the comp-set bell-curve. Three decision-classes: structurally optimistic, structurally pessimistic, or outside-bell-curve.
Risk taxonomy
Six AI agents (research / market / financial / risk / competitor / synthesis) each scope a different failure-mode. Output is the consolidated synthesis with named risks and named mitigations.
Go/no-go read
A structured downloadable PDF with the full agent-team reasoning chain. You can defend the decision to a co-founder, an investor, or your future self.
Depth vs breadth
Most validation tools scrape “live signals” — Reddit threads, Twitter sentiment, Crunchbase funding stages, Google Trends curves. That's breadth. We work from the cohort-comp-set data investors actually use:
| Methodology dimension | Category-typical AI validators | DimeADozen.AI |
|---|---|---|
| Data source | Live signal scraping (Reddit + Twitter + forums + funding DBs) | Public S-1 filings + earnings reports + named comp-set economics |
| Output depth | Viability score + competitor map + risk flags | Sourced data + named comp-set + S-1 retention curves + cohort unit-economics + structured go/no-go read |
| Granularity | Surface signals (what people are saying) | Cohort behavior (how comparable businesses actually performed) |
| Investor-readiness | Founder-comfort grade | Investor-grade depth |
| Pricing model | $5–$29 per report + free-tier | $129 once — pure validation specialist |
$129 vs cheaper alternatives
$29 validation tools answer “what are people saying about my idea right now?” That's useful for founder-comfort.
$129 DimeADozen.AI answers “how have cohort-comparable businesses actually performed in their real customer retention curves and unit-economics?” That's investor-grade depth.
Both have a place. The question is which one you need at your stage.
- If you're brainstorming: free-tier idea-score tools work. They scrape signals + give a temperature read.
- If you're committing build-time: you need the depth. S-1 retention-curve math + named comp-set unit-economics show how comparable businesses actually retained their cohorts — the difference between “scoring 70+ on sentiment” and “scoring 70+ on cohort-economics.”
- If you're going into a YC interview or fundraise: you need investor-grade depth. $129 once for the report; $499 Founder Strategy Call for the 1:1; $2K VC-Ready Diligence Pack for the deep pre-raise prep.
We publish the actual report shape
So you can audit it before paying.
Sample 1: Munchery autopsy
- How a $100M+ funded company died on growth-stage retention math.
- S-1 retention curve plotted against five comp-set meal-kit companies.
- Risk taxonomy: liquid-cold-chain margin-floor, repeat-customer disambiguation, regulatory-contract dependency.
Sample 2: Juicero autopsy
- How a $100M+ funded company died on differentiated-hardware-ROI math.
- S-1 retention curve plotted against three comp-set hardware-SaaS companies.
- Risk taxonomy: hardware-margin-floor, competing-substitute-cost, distribution-channel concentration.
Sourced data + named comp-set + retention-curve math is the work.
Not a chatbot to argue with.
Not a course to work through.
A structured downloadable decision document.
Pressure-test your own idea:
We’ve been mentioned in the press
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Bi-weekly. Two sections: What landed this week (a specific founder-validation decision) and Math you missed (a quantitative framework with named comp-set citations). Sourced research, not paraphrase.